What is your personal experience of the difference in the way basic science research is conducted in the USA and India?

As a biomedical researcher, I consider the research I did during my Ph.D. in India to be the most rigorous by far. It was the only project where statistics were appropriately and correctly applied right from the first step, the experiment design, continuing with blinding of the samples through to data analysis.

Goal of my Ph.D. project was to figure out if prior exposure to environmental mycobacteria (NTM, Nontuberculous mycobacteria) could explain why the largest TB vaccine trial had failed to protect against adult pulmonary TB. Conducted from 1967 to 1980 on ~360000 people across 209 villages and 1 town in South India, prior exposure to environmental mycobacteria emerged as a plausible reason. Only there was no data on NTM in this environment, if yes, what species and where, in the soil/water/dust. I was just one person. How could I cover such a vast population over such a vast area? That's where statistics entered the picture, exactly where it should, in the experimental design itself. A professional statistician crunched the numbers to determine how many villages I should cover, how many houses per village, which villages, i.e., make sure I comprehensively sampled the entire trial area in as unbiased a manner as possible. Starting with this design, he carefully shepherded every step of my Ph.D. project and even blinded the samples I brought back from the field, only decoding them after I'd generated all the data. Since I don't have any other experience on basic research in India, I don't know if my experience if generalizable so I'll leave it at that. 

Moving on from differences between India and US, I'll highlight two dubious practices that are rampant in basic biomedical research the world over, at least if we go by the published literature. Overarching problem consists of two features

1. Statistics are misused, usually applied only at the back end to analyze the data after it's been generated, instead of the optimal approach which is to apply them from the beginning in the experiment design itself.

2.Definition of scientific misconduct is too narrow, completely ignoring the most prevalent practice, which isn't outright fraud but rather data selection.

Compared to basic research, rigorous statistical science applied to human clinical trials is the norm. Only very slowly is this mindset permeating into basic research to replace this ridiculous state of affairs. Last year, we saw the publication of the first randomized clinical trial in mice (1). 

The US ORI (United States Office of Research Integrity) defines Scientific misconduct as consisting of data fabrication, data falsification or plagiarism. But far more than any of these, the most prevalent practice is something that's not even on the radar, data selection, i.e., cherry-picking data. Practice is rampant. Rarely do animal model studies show data combined from different experiments. Take a look at any recent paper, even ones published in Nature or Science. Invariably a figure legend would say something along the lines of, 'Data from one representative experiment out of 3, 4 or 5 different experiments is shown'. Why not show combined data from all experiments performed? How could such a shoddy practice be the norm? Simply means intra-group variation between experiments was greater than inter-group variation within one single experiment. Either experimenters are shoddy or techniques too unrefined. Either way, cannot trust such data. And this is still the norm in basic biomedical research.  

Bibliography:

1.  Llovera, Gemma, et al. "Results of a preclinical randomized controlled multicenter trial (pRCT): Anti-CD49d treatment for acute brain ischemia." Science Translational Medicine 7.299 (2015): 299ra121-299ra121. http://stm.sciencemag.org/conten...

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